AIX360

AI Explainability Tool

A toolkit for explaining complex AI models and data-driven insights

Interpretability and explainability of data and machine learning models

GitHub

2k stars
56 watching
308 forks
Language: Python
last commit: about 2 years ago
Linked from 3 awesome lists

artificial-intelligencecodaitdeep-learningexplainabilexplainable-aiexplainable-mlibm-researchibm-research-aimachine-learningtrusted-aitrusted-mlxai

Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
andreysharapov/xaienceAn online repository providing resources and information on explainable AI, algorithmic fairness, ML security, and related topics107
understandable-machine-intelligence-lab/quantusAn eXplainable AI toolkit for evaluating and interpreting neural network explanations in various deep learning frameworks.567
ethicalml/xaiAn eXplainability toolbox for machine learning that enables data analysis and model evaluation to mitigate biases and improve performance1,135
h2oai/mli-resourcesProvides tools and techniques for interpreting machine learning models483
pbiecek/xai_resourcesA collection of resources and papers related to Explainable Artificial Intelligence (XAI) for machine learning model interpretability.819
pbiecek/xaiaterum2020An R package and workshop materials for explaining machine learning models using explainable AI techniques52
deel-ai/xpliqueAn Explainable AI toolbox that provides various methods and tools to understand and interpret the behavior of neural networks654
simpleai-team/simpleaiProvides tools and utilities for implementing various artificial intelligence algorithms968
dianna-ai/diannaA Python package providing an explainable AI interface to research projects49
explainx/explainxA framework to explain and debug blackbox machine learning models with a single line of code.419
modeloriented/dalexA tool to help understand and explain the behavior of complex machine learning models1,390
jphall663/interpretable_machine_learning_with_pythonTeaching software developers how to build transparent and explainable machine learning models using Python673
jphall663/responsible_xaiGuidelines and resources for the development of responsible AI systems17
howiehwong/trustllmA toolkit for assessing trustworthiness in large language models491
interpretml/diceProvides counterfactual explanations for machine learning models to facilitate interpretability and understanding.1,373